def main():
msg = "usage: ./p2p2012.py type r|g|g2 ttl par tries churn_rate"
if len(sys.argv) < 7:
print msg
sys.exit(1)
global out_file, churn_rate
out_file = sys.stdout
gtype = sys.argv[1]
walk = sys.argv[2]
ttl = int(sys.argv[3])
par = int(sys.argv[4])
tries = int(sys.argv[5])
churn_rate = float(sys.argv[6])
if gtype == "a":
g = nx.barabasi_albert_graph(97134, 3)
elif gtype == "b":
g = nx.barabasi_albert_graph(905668, 12)
elif gtype == "c":
g = sm.randomWalk_mod(97134, 0.90, 0.23)
elif gtype == "d":
g = sm.randomWalk_mod(905668, 0.93, 0.98)
elif gtype == "e":
g = sm.nearestNeighbor_mod(97134, 0.53, 1)
elif gtype == "f":
g = sm.nearestNeighbor_mod(905668, 0.90, 5)
elif gtype == "g":
g = nx.random_regular_graph(6, 97134)
elif gtype == "h":
g = nx.random_regular_graph(20, 905668)
elif gtype == "i":
g = nx.read_edgelist(sys.argv[7])
if walk == "r":
lookup(g, ttl, tries, par, get_random_node)
elif walk == "g":
lookup(g, ttl, tries, par, get_greedy_node)
elif walk == "g2":
lookup(g, ttl, tries, par, get_greedy2_node)
elif walk == "sum":
sum_edges(g, int(sys.argv[3]))
nodes = g.number_of_nodes()
edges = g.size()
avg_cc = nx.average_clustering(g)
print >> sys.stderr, nodes, edges, avg_cc
def fit_nearestNeighbor_mod(graphSize, graphID, dkPath, original2k, resultPath,
k):
"""
Runs synthetic graph tests for various 'k' and 'u' values and stores them
"""
if k:
outfile = open(resultPath + graphID + '_nnFine_dkDistances.txt', 'w')
kList = [k]
uList = []
myU = 0.01
while myU < 1:
uList.append(myU)
myU += 0.01
else:
outfile = open(resultPath + graphID + '_nnCoarse_dkDistances.txt', 'w')
kList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
uList = [
.01, .05, .10, .20, .25, .30, .35, .40, .45, .50, .55, .60, .65,
.70, .75, .80, .85, .95
]
outfile.write('dk-2 Distance\tk\tu\n')
for k in kList:
for u in uList:
print 'Running modified Nearest Neighbor with parameters: n = ', graphSize, ' k = ', k, ' u = ', u
newFile = graphID + '_nn_' + str(k) + '_' + str(u)
# Create synthetic graph
syntheticGraph = sm.nearestNeighbor_mod(graphSize, u, k)
# Write pickle, edge list, and 2k distro to file
print 'Writing pickle and calculating dK-2...\n'
nx.write_gpickle(syntheticGraph, resultPath + newFile + '.pickle')
getdk2(syntheticGraph, newFile, dkPath, resultPath)
# Find distance between the dK-2 distributions
dkDistance = tk.get_2k_distance(
original2k, resultPath + newFile + '_target.2k')
outfile.write(
str(dkDistance) + '\tk = ' + str(k) + '\tu = ' + str(u) + '\n')
outfile.flush()
outfile.write('\n')
outfile.close()
def fit_nearestNeighbor_mod(graphSize, graphID, dkPath, original2k, resultPath, k):
"""
Runs synthetic graph tests for various 'k' and 'u' values and stores them
"""
if k:
outfile = open(resultPath + graphID + '_nnFine_dkDistances.txt', 'w')
kList = [k]
uList = []
myU = 0.01
while myU < 1:
uList.append(myU)
myU += 0.01
else:
outfile = open(resultPath + graphID + '_nnCoarse_dkDistances.txt', 'w')
kList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
uList = [.01, .05, .10, .20, .25,.30, .35, .40, .45, .50,
.55, .60, .65, .70, .75,.80, .85, .95]
outfile.write('dk-2 Distance\tk\tu\n')
for k in kList:
for u in uList:
print 'Running modified Nearest Neighbor with parameters: n = ', graphSize, ' k = ', k, ' u = ', u
newFile = graphID + '_nn_' + str(k) + '_' + str(u)
# Create synthetic graph
syntheticGraph = sm.nearestNeighbor_mod(graphSize, u, k)
# Write pickle, edge list, and 2k distro to file
print 'Writing pickle and calculating dK-2...\n'
nx.write_gpickle(syntheticGraph, resultPath + newFile + '.pickle')
getdk2(syntheticGraph, newFile, dkPath, resultPath)
# Find distance between the dK-2 distributions
dkDistance = tk.get_2k_distance(original2k, resultPath + newFile + '_target.2k')
outfile.write(str(dkDistance) + '\tk = ' + str(k) + '\tu = ' + str(u) + '\n')
outfile.flush()
outfile.write('\n')
outfile.close()
